Mary Demerick, the COO of Swank Retailers in Phoenix, AZ, is busy looking over the most recent sales information for the company. She has called a meeting of all salespersons in the region one week from today, and she is attempting to estimate the sales level that should be expected for their company over the next three months. She needs to have this information so that the sales quotas can be set for the individual salespersons. Her staff has accumulated historical sales data as shown in the Table on the next page. It is obvious to Mrs. Demerick that Swank's sales have a definite seasonal pattern from month to month. The nature of the retailing business causes the seasonal patterns, and the population growth of the region is probably responsible for the overall growth in sales. Mrs. Demerick expects these patterns and trends to continue.

1. Use an α of 0.8 and develop an exponential smoothing forecast for the next month's sales (Year 4, Jan.).

2. Use seasonalized time series analysis to develop a forecast for the next three month's sales, i.e., Year 4, Jan, Feb and March (assume monthly seasons).

Month	Period	Time Period	Actual Demand
Year 1	Jan	1	4.9
Year 1	Feb	2	6.1
Year 1	Mar	3	7.5
Year 1	Apr	4	7.4
Year 1	May	5	5.2
Year 1	Jun	6	5.3
Year 1	Jul	7	5.6
Year 1	Aug	8	7.1
Year 1	Sep	9	8
Year 1	Oct	10	6.7
Year 1	Nov	11	8.2
Year 1	Dec	12	7.5
Year 2	Jan	13	5.1
Year 2	Feb	14	6.3
Year 2	Mar	15	7.9
Year 2	Apr	16	8
Year 2	May	17	5.5
Year 2	Jun	18	5.9
Year 2	Jul	19	6.3
Year 2	Aug	20	7.7
Year 2	Sep	21	8.5
Year 2	Oct	22	7.1
Year 2	Nov	23	8.9
Year 2	Dec	24	8.7
Year 3	Jan	25	5.4
Year 3	Feb	26	7.5
Year 3	Mar	27	8.2
Year 3	Apr	28	8.7
Year 3	May	29	6.1
Year 3	Jun	30	6.3
Year 3	Jul	31	6.9
Year 3	Aug	32	8.5
Year 3	Sep	33	9
Year 3	Oct	34	8.1
Year 3	Nov	35	10.2
Year 3	Dec	36	9.5